Carbon multi-walled nanotubes grown by HWCVD on a pyroelectric detector

Carbon multi-wall nanotubes (MWNTs) were grown on a lithium niobate (LiNbO₃) pyroelectric detector with a nickel film as the catalyst by hot-wire chemical vapor deposition (HWCVD). Two detectors are documented, each with slightly different deposition conditions. The absolute spectral responsivity of each device was measured from 600 nm to 1800 nm and indicates that the MWNT-coating absorptance is spectrally uniform, with variations of only a few percent. We also discuss growth of MWNTs on LiTaO₃ by CVD and the limitations presented by the Curie temperature of the pyroelectric material.     

Cp*RhIII-Catalyzed Sterically Controlled C(sp3)−H Selective Mono- and Diarylation of 8-Methylquinolines with Organoborons**

Herein, the Rh^III-catalyzed selective monoarylation and diarylation (symmetrical and unsymmetrical) of 8-methylquinolines with organoboron reagents are disclosed. The selective monoarylation of primary C(sp³)−H bonds is achieved by using 7-substituted 8-methylquinolines or by changing the quantity of the aryl boronic acids. The method is also applicable for the arylation of 2-ethylpyridines, and the heteroarylation with thiophene-2-ylboronic acids. Symmetrical and unsymmetrical diarylation of 8-methylquinolines have been carried out in one-pot and sequential manner, respectively. Late-stage monoarylation of oxime derivatives and gram-scale synthesis of monoarylated products has also been carried out. A mechanistic study revealed that the current reaction is first order with respect to both reactants and a five-membered rhodacycle intermediate may be involved in the catalytic cycle.     

Aptamer-mediated colorimetric and electrochemical detection of Pseudomonas aeruginosa utilizing peroxidase-mimic activity of gold NanoZyme

Analytical and Bioanalytical Chemistry - Despite of various advancements in biosensing, a rapid, accurate, and on-site detection of a bacterial pathogen is a real challenge due to the lack of...     

Energy-Effective and Secure Data Transfer Scheme for Mobile Nodes in Smart City Applications

Wireless Personal Communications - Mobile nodes are deployed at different locations in the smart city to collect meaningful information so captured data can be used as an input in different smart...     

Kalman filtering for reliable estimation of BBB permeability

INTRODUCTION: The blood-brain barrier (BBB) plays an important role in the pathophysiology of a number of central nervous system disorders. In the past, a number of laboratory techniques have been proposed to quantify permeability coefficient ki, an important index of barrier function. Recently, magnetic resonance imaging (MRI) has been used to estimate ki based on graphical plot technique. The MR technique was found to be in good agreement with the gold standard, quantitative autoradiography (QAR). However, a reduced image signal-to-noise ratio, among other factors such as partial volume effects, did not allow reliable estimation of permeability coefficients. This proof-of-principle study proposes the use of Kalman filter as a filtering technique for a reliable estimation of permeability coefficients. The results are compared to those obtained using the Wiener filter technique. MATERIALS AND METHODS: MRI experiments were performed in Wistar rats (N=2) using a 4.7-T Bruker Biospec MR system (Bruker Biospin, Billerica, MA). After acquiring localizer images, T2-weighted diffusion-weighted imaging images were acquired. Finally, a rapid T1 mapping protocol was implemented to acquire one pre-gadolinium diethylenetriamine pentaacetic acid baseline data set followed by postinjection data sets at 3-min intervals for 45 min. Data were postprocessed with and without the application of Kalman and Wiener filters to obtain an estimate of ki. RESULTS AND DISCUSSION: Comparing T1 maps, Patlak plots and permeability maps with and without the Kalman filtering presented several interesting observations. Kalman-filtered Patlak plots, compared to nonfiltered plots, showed that discrete data points on the plot were closer to the line fit. The number of time points used for the construction of the graphical plot had no effect on permeability coefficient estimates when the Kalman filter was used. A box-and-whiskers plot showed longer Y-error bars for nonfiltered and Wiener data compared to Kalman-filtered data. These observations suggest that it may be possible to obtain reliable permeability coefficient estimates in a short study time by applying the Kalman filter to the data. Future work involves investigating the application of this filter on a large-sample-size animal study and evaluating the role of partial volume effects on BBB permeability estimation.     

Artificial Neural Network (ANN) design for Hg–Se interactions and their effect on reduction of Hg uptake by radish plant

The tendency of selenium to interact with heavy metals in presence of naturally occurring species has been exploited for the development of green bioremediation of toxic metals from soil using Artificial Neural Network (ANN) modeling. The cross validation of the data for the reduction in uptake of Hg(II) ions in the plant R. sativus grown in soil and sand culture in presence of selenium has been used for ANN modeling. ANN model based on the combination of back propagation and principal component analysis was able to predict the reduction in Hg uptake with a sigmoid axon transfer function. The data of fifty laboratory experimental sets were used for structuring single layer ANN model. Series of experiments resulted into the performance evaluation based on considering 20% data for testing and 20% data for cross validation at 1,500 Epoch with 0.70 momentums The Levenberg–Marquardt algorithm (LMA) was found as the best of BP algorithms with a minimum mean squared error at the eighth place of the decimal for training (MSE) and cross validation.